Rule-based machine learning

Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves `rules’ to store, manipulate or apply, knowledge. The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learners that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based machine learning approaches include learning classifier systems,association rule learning,artificial immune systems, and any other method that relies on a set of rules, each covering contextual knowledge. Rule-based machine learning is distinct from related rule-based systems and other rule-based decision makers. This is because rule-based machine learning applies some form of learning algorithm to automatically identify useful rules, rather than a human needing to apply prior domain knowledge to manually construct rules and curate a rule set.

Rules typically take the form of an {IF:THEN} expression, (e.g. {IF ‘condition’ THEN ‘result’}, or as a more specific example, {IF ‘red’ AND ‘octagon’ THEN ‘stop-sign’}). An individual rule is not in itself a model, since the rule is only applicable when its condition is satisfied. Therefore rule-based machine learning methods typically identify a set of rules that collectively comprise the prediction model, or the knowledge base.

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